A genetic based scheduling approach of real-time reconfigurable embedded systems

This paper deals with the problem of scheduling the mixed workload of both homogeneous multiprocessor on-line and off-line periodic tasks in a critical reconfigurable real-time environment by a genetic algorithm. Two forms of automatic reconfigurations which are assumed to be applied at run-time: Addition-Removal of tasks or just modifications of their temporal parameters: worst case execution time (WCET) and/or deadlines. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. We define an Intelligent Agent that automatically checks the system's feasibility after any reconfiguration scenario to verify if all tasks meet the required deadlines after a reconfiguration scenario was applied on a multiprocessor embedded real-time system. Indeed, if the system is unfeasible, then the proposed genetic algorithm dynamically provides a solution that meets real-time constraints. This genetic algorithm based on a highly efficient decoding procedure, strongly improves the quality of real-time scheduling in a critical environment. The effectiveness and the performance of the designed approach is evaluated through simulation studies illustrated by testing Hopper's benchmark results.